A Blueprint for Scalable & Reliable Enterprise AI/ML Systems // Panel // AIQCON
// Abstract
Enterprise AI leaders continue to explore the best productivity solutions that solve business problems, mitigate risks, and increase efficiency. Building reliable and secure AI/ML systems requires following industry standards, an operating framework, and best practices that can accelerate and streamline the scalable architecture that can produce expected business outcomes. This session, featuring veteran practitioners, focuses on building scalable, reliable, and quality AI and ML systems for the enterprises.
// Panelists
- Hira Dangol: VP, AI/ML and Automation @ Bank of America
- …
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Chapters (11)
Panelists discuss vision and strategy in AI
5:18
Steven Eliuk, IBM expertise in data services
7:30
AI as means to improve business metrics
11:10
Key metrics in production systems: efficiency and revenue
13:50
Consistency in data standards aids data integration
17:47
Generative AI presents new data classification risks
22:47
Evaluating implications, monitoring, and validating use cases
26:41
Evaluating natural language answers for efficient production
29:10
Monitoring AI models for performance and ethics
31:14
AI metrics and user responsibility for future models
34:56
Access to data is improving, promising progress
Playlist
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Our 1st MLOps Meetup // Luke Marsden // MLOps Meetup #1
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Remote Collaboration as a Data Scientist
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MLOps Manifesto with Luke Marsden from Dotscience
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MLOps lifecycle description
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What Does Best in Class AI/ML Governance Look Like in Fin Services? // Charles Radclyffe // MLOps #2
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Automation, UBI, and taxes with Charles Radclyffe
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Common sense AI/ML governance with Charles Radclyffe
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Life purpose and too many spreadsheets
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Explainability, Black boxes and EU white paper on reproducibility
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The problem with too many smart ML engineers
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What should we be optimizing for?
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Hierarchy of Machine Learning Needs // Phil Winder // MLOps Meetup #3
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Automatically Retrain Machine Learning Models? Are best practices worth it?
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Building an MLOps Team? Key ideas to keep in mind
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Hierarchy of MLOps Needs
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Bare necessities for getting an ML model into production
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MLOps and Monitoring
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How Phil Winder got into Data Science and Software Engineering
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Provenance and Reproducibility in Machine Learning; what is it and why you need it?
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Friction Between Data Scientists and Software Engineers
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MLOps Problems in different size companies
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ML tooling in large companies
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ML Platforms - The build vs buy question
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ML Services Gateway at SurveyMonkey
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Message buses, Async and sync architecture
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MLOps #4: Shubhi Jain - Building an ML Platform @SurveyMonkey
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Hybrid Data Science Teams @SurveyMonkey
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How do you handle ML version control at SurveyMonkey
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Doing ML with Personal Information
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Evolution of the ML feature store @SurveyMonkey
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Developing a Machine Learning Feature Store
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Auto retrain ML models is not the question
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3 key parts to Machine Learning monitoring
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MLOps Meetup #6: Mid-Scale Production Feature Engineering with Dr. Venkata Pingali
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MLOps meetup #5 High Stakes ML: Active Failures, Latent Factors with Flavio Clesio
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MLOps: Airflow Pros and Cons
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Specific challenges in Machine Learning
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Current State Of Machine Learning
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Humans in the Loop are a defining factor in Machine Learning
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Learning from real life Machine Learning failures
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Survivorship Bias in machine learning tutorials
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Swiss Cheese model in Machine Learning
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Resume driven development in Machine learning & software engineering
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Who has the highest standards in ML?
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Venkata Pingali of Scribble Data Thoughts on the Current State of Machine Learning
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Dependable data and being able to Trust in your Data with Venkata Pengali of Scribble Data
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Speed, Trust, Evolution and Scale in MLOps
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More difficult transition for data scientists to become ML engineers
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How many models in prod til I need a dedicated ML platform?
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Deeper thinking from data scientists around platform blackholes
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Checkpointing, metadata, and confidence in your data
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Adjacent usecases and multistep feature engineering
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Standardization of Machine Learning tools like in Software Engineering with Venkata Pingali
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Reproducability flaws in end to end Machine Learning debugging
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3rd wave of data scientists
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MLOps meetup #7 Alex Spanos // TrueLayer 's MLOps Pipeline
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MLOps Meetup #8 Optimizing Your ML Workflow with Kubeflow 1.0
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Are Kubeflow and Airflow complementary?
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Why Kubeflow gained so much traction=open community
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Who decides the dirrection of Kubeflow
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DeepCamp AI